DATA QUALITY
Build Confidence in Sustainability Data with “Four Eyes” Approval Workflows
GLYNT.AI’s approval workflows for sustainability data apply the same discipline as financial accounting: two managers check, reconcile and sign off on data before it’s reported, creating data that your team and your auditors can rely on.
REVIEW EFFICIENTLY
Monitor sustainability data quality, reconcile outliers and approve changes
Monitor sustainability data quality, reconcile outliers and approve changes
GLYNT.AI’s pre-built reports surface anomalies that need a second look. Every review and approval is logged automatically, building a clear audit trail.
Review data quality
Use GLYNT.AI’s pre-built reports, no need to dig through the raw data yourself.
Reconcile outliers
Trace the root cause of each outlier, and leave contextual notes for operations teams and auditors.
Approve changes
Review new sites, vendors or data feeds before they’re added to your sustainability data.
WHY THIS MATTERS
Why sustainability teams need a data approval process
Structured reviews save time, and catch errors automation alone can miss, and build the audit trail regulators increasingly expect.
BenefitDescription
Save time
Pre-built reports save hours each month
Increase accuracy
GLYNT.AI surfaces issues automatically
Increase context
Reconcile outliers to real-world events
Increase stakeholder confidence
Match what is expected of financial data
End-to-end traceability
Track from reportable data to the original source files and data
Meet audit requirements
Audit and assurance standards increasingly expect documented review and approval
APPROVAL PROCESS
The sustainability data approval process is five steps, repeated monthly or quarterly
The approval process uses the same “four eyes” review as financial data, and once both reviews are complete, the record locks against further changes
| Step | Description | Who | Outcome |
|---|---|---|---|
| 1 | Prepare data | GLYNT.AI | Approval package sent to customer |
| 2 | Review, reconcile and approve data | Customer | Data reviewed for quality; outliers noted and explained; data approved to move forward |
| 3 | Update data (as needed) | GLYNT.AI | Data updated based on reviewer notes |
| 4 | Review data in context | Customer | Data approved in reporting software or custom reports |
| 5 | Lock data | Customer | Data locked; further changes require senior management approval |
FAQS
Approval workflows are new to most sustainability teams, get your questions answered
Explore the FAQs below to learn more about a monthly or quarterly approval workflow in sustainability
How often should sustainability data be reviewed and approved?
Most companies run the approval cycle monthly or quarterly, matching their existing financial close calendar.
What happens if an approver finds an error or outlier?
The approver flags the issue, notes the cause, and sends it back for a fix with notes explaining the outlier. Typically, the issue is not a data error, but a real-world issue, such as a huge water usage spike due to a leak. The approval process includes notes on the source of the issue, creating valuable context for all users.
Can locked sustainability data be changed after approval?
Yes, but once data is locked a senior manager must authorize the change. This protects the integrity of published reports and creates a clear record of who approved what, and when, which is exactly what auditors look for when testing data controls.
Why use a "four eyes" model instead of a single reviewer?
The first reviewer — typically a mid-level manager — knows the site details, while a senior manager spots patterns across the whole portfolio. Two independent reviews catch more than either role could alone, which is why financial accounting has used this model for decades.
Do auditors require a formal sustainability data approval process?
This is not a universal requirement yet, but assurance and audit standards are rapidly moving sustainability reporting toward the same audit rigor as financial reporting. Auditors already ask for evidence of review and reconciliation, and a documented approval workflow is the clearest way to show that evidence exists.
How is an approval workflow different from an automated data quality check?
Automated checks flag anomalies; they don’t explain them. An approval workflow adds the human context that automation can’t provide, such as documenting a water leak. Context connects data values to the real-world, which is at the heart of sustainability.